Subscribe free to our newsletters via your
. 24/7 Space News .

Subscribe free to our newsletters via your

Chip could bring deep learning to mobile devices
by Staff Writers
Boston MA (SPX) Feb 04, 2016

MIT researchers have designed a new chip to implement neural networks. It is 10 times as efficient as a mobile GPU, so it could enable mobile devices to run powerful artificial-intelligence algorithms locally, rather than uploading data to the Internet for processing. Image courtesy MIT News.

In recent years, some of the most exciting advances in artificial intelligence have come courtesy of convolutional neural networks, large virtual networks of simple information-processing units, which are loosely modeled on the anatomy of the human brain.

Neural networks are typically implemented using graphics processing units (GPUs), special-purpose graphics chips found in all computing devices with screens. A mobile GPU, of the type found in a cell phone, might have almost 200 cores, or processing units, making it well suited to simulating a network of distributed processors.

At the International Solid State Circuits Conference in San Francisco this week, MIT researchers presented a new chip designed specifically to implement neural networks. It is 10 times as efficient as a mobile GPU, so it could enable mobile devices to run powerful artificial-intelligence algorithms locally, rather than uploading data to the Internet for processing.

Neural nets were widely studied in the early days of artificial-intelligence research, but by the 1970s, they'd fallen out of favor. In the past decade, however, they've enjoyed a revival, under the name "deep learning."

"Deep learning is useful for many applications, such as object recognition, speech, face detection," says Vivienne Sze, an assistant professor of electrical engineering at MIT whose group developed the new chip.

"Right now, the networks are pretty complex and are mostly run on high-power GPUs. You can imagine that if you can bring that functionality to your cell phone or embedded devices, you could still operate even if you don't have a Wi-Fi connection. You might also want to process locally for privacy reasons. Processing it on your phone also avoids any transmission latency, so that you can react much faster for certain applications."

The new chip, which the researchers dubbed "Eyeriss," could also help usher in the "Internet of things" - the idea that vehicles, appliances, civil-engineering structures, manufacturing equipment, and even livestock would have sensors that report information directly to networked servers, aiding with maintenance and task coordination.

With powerful artificial-intelligence algorithms on board, networked devices could make important decisions locally, entrusting only their conclusions, rather than raw personal data, to the Internet. And, of course, onboard neural networks would be useful to battery-powered autonomous robots.

Division of labor
A neural network is typically organized into layers, and each layer contains a large number of processing nodes. Data come in and are divided up among the nodes in the bottom layer. Each node manipulates the data it receives and passes the results on to nodes in the next layer, which manipulate the data they receive and pass on the results, and so on. The output of the final layer yields the solution to some computational problem.

In a convolutional neural net, many nodes in each layer process the same data in different ways. The networks can thus swell to enormous proportions. Although they outperform more conventional algorithms on many visual-processing tasks, they require much greater computational resources.

The particular manipulations performed by each node in a neural net are the result of a training process, in which the network tries to find correlations between raw data and labels applied to it by human annotators. With a chip like the one developed by the MIT researchers, a trained network could simply be exported to a mobile device.

This application imposes design constraints on the researchers. On one hand, the way to lower the chip's power consumption and increase its efficiency is to make each processing unit as simple as possible; on the other hand, the chip has to be flexible enough to implement different types of networks tailored to different tasks.

Sze and her colleagues - Yu-Hsin Chen, a graduate student in electrical engineering and computer science and first author on the conference paper; Joel Emer, a professor of the practice in MIT's Department of Electrical Engineering and Computer Science, and a senior distinguished research scientist at the chip manufacturer NVidia, and, with Sze, one of the project's two principal investigators; and Tushar Krishna, who was a postdoc with the Singapore-MIT Alliance for Research and Technology when the work was done and is now an assistant professor of computer and electrical engineering at Georgia Tech - settled on a chip with 168 cores, roughly as many as a mobile GPU has.

Act locally
The key to Eyeriss's efficiency is to minimize the frequency with which cores need to exchange data with distant memory banks, an operation that consumes a good deal of time and energy. Whereas many of the cores in a GPU share a single, large memory bank, each of the Eyeriss cores has its own memory. Moreover, the chip has a circuit that compresses data before sending it to individual cores.

Each core is also able to communicate directly with its immediate neighbors, so that if they need to share data, they don't have to route it through main memory. This is essential in a convolutional neural network, in which so many nodes are processing the same data.

The final key to the chip's efficiency is special-purpose circuitry that allocates tasks across cores. In its local memory, a core needs to store not only the data manipulated by the nodes it's simulating but data describing the nodes themselves. The allocation circuit can be reconfigured for different types of networks, automatically distributing both types of data across cores in a way that maximizes the amount of work that each of them can do before fetching more data from main memory.

At the conference, the MIT researchers used Eyeriss to implement a neural network that performs an image-recognition task, the first time that a state-of-the-art neural network has been demonstrated on a custom chip.


Related Links
Massachusetts Institute of Technology
All about the robots on Earth and beyond!

Comment on this article via your Facebook, Yahoo, AOL, Hotmail login.

Share this article via these popular social media networks DiggDigg RedditReddit GoogleGoogle

Previous Report
Thales, ASV to jointly develop unmanned surface vehicle technology
Washington (UPI) Jan 27, 2016
Thales and ASV have signed an agreement to cooperate on the development of unmanned surface vehicle technology. The announcement by the two companies to collaborate on the development follows the completion of sea trials for ASV's Halcyon unmanned surface vehicle. The vehicle is designed for both civil and military purposes. "Thales has already delivered world leading autonomous ... read more

Russia postpones manned Lunar mission to 2035

Audi joins Google Lunar XPrize competition

Lunar mission moves a step closer

Momentum builds for creation of 'moon villages'

Mars Rover Opportunity Busy Through Depth of Winter

India to Cooperate With France on Next Mission to Mars

Opportunity rock abrasion tool conducts two rock grinds

Curiosity gets a good taste of scooped, sieved sand

Challenger disaster at 30: Did the tragedy change NASA for the better?

Voyager Mission Celebrates 30 Years Since Uranus

Arab nations eye China, domestic market to revive tourism

2016 Goals Vital to Commercial Crew Success

China aims for the Moon with new rockets

China shoots for first landing on far side of the moon

Chinese Long March 3B to launch Belintersat-1 telco sat for Belarus

China Plans More Than 20 Space Launches in 2016

Russian Cosmonauts to Attach Thermal Insulation to ISS

Astronaut Scott Kelly plays ping pong with water

Japanese astronaut learned Russian to link two nations

NASA, Texas Instruments Launch mISSion imaginaTIon

70th consecutive successful launch for Ariane 5

AMOS-6 Scheduled for May 2016 Launch by Space-X

SpaceX Tests Crew Dragon Parachutes

Arianespace's year-opening Ariane 5 mission is approved for launch

Astronomers discover largest solar system

Lonely Planet Finds a Mum a Trillion Km Away

Follow A Live Planet Hunt

Lab discovery gives glimpse of conditions found on other planets

Energy harvesting via smart materials

Imaged 'jets' reveal cerium's post-shock inner strength

ChemChina 'eyeing Syngenta' in biggest ever Chinese takeover

Controlling the magnetic properties of individual iron atom

Memory Foam Mattress Review
Newsletters :: SpaceDaily :: SpaceWar :: TerraDaily :: Energy Daily
XML Feeds :: Space News :: Earth News :: War News :: Solar Energy News

The content herein, unless otherwise known to be public domain, are Copyright 1995-2016 - Space Media Network. All websites are published in Australia and are solely subject to Australian law and governed by Fair Use principals for news reporting and research purposes. AFP, UPI and IANS news wire stories are copyright Agence France-Presse, United Press International and Indo-Asia News Service. ESA news reports are copyright European Space Agency. All NASA sourced material is public domain. Additional copyrights may apply in whole or part to other bona fide parties. Advertising does not imply endorsement, agreement or approval of any opinions, statements or information provided by Space Media Network on any Web page published or hosted by Space Media Network. Privacy Statement All images and articles appearing on Space Media Network have been edited or digitally altered in some way. Any requests to remove copyright material will be acted upon in a timely and appropriate manner. Any attempt to extort money from Space Media Network will be ignored and reported to Australian Law Enforcement Agencies as a potential case of financial fraud involving the use of a telephonic carriage device or postal service.